Modern inventory systems, such as those in mail order warehouses, supply chain distribution centers, airport luggage systems, and custom-order manufacturing facilities, face significant challenges in responding to requests for inventory items. As inventory systems grow, the challenges of simultaneously completing a large number of packing, storing, and other inventory-related tasks become non-trivial. In inventory systems tasked with responding to large numbers of diverse inventory requests, inefficient utilization of system resources, including space, equipment, and manpower, can result in lower throughput, unacceptably long response times, an ever-increasing backlog of unfinished tasks, and, in general, poor system performance. Additionally, expanding or reducing the size or capabilities of many inventory systems requires significant changes to existing infrastructure and equipment. As a result, the cost of incremental changes to capacity or functionality may be prohibitively expensive, limiting the ability of the system to accommodate fluctuations in system throughput.
Additionally, in high-capacity inventory systems (e.g., inventory systems that manage thousands of items per day), there is a need to optimize the storage of inventory items in order to better utilize the storage space available. In these inventory systems, the operator of a high-capacity inventory system may need to optimize gross cubic utilization (GCU) of storage space. In inventory systems that utilize robotic system components for item storage and retrieval, items maintained in the inventory system are often stored in a way that does not optimize GCU (e.g., there is significant space between the items) since it is difficult for robotic system components to stow items in tight spaces. This is made more difficult in that conventional robots are often unaware of force limits associated with particular items. Without knowledge of those force limits, these conventional robots are unable to optimize GCU by tightly packing inventory.